Computer Science > Computer Vision and Pattern Recognition
arXiv:2603.08997 (cs)
[Submitted on 9 Mar 2026]
Title:SkipGS: Post-Densification Backward Skipping for Efficient 3DGS Training
View a PDF of the paper titled SkipGS: Post-Densification Backward Skipping for Efficient 3DGS Training, by Jingxing Li and 2 other authors
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Abstract:3D Gaussian Splatting (3DGS) achieves real-time novel-view synthesis by optimizing millions of anisotropic Gaussians, yet its training remains expensive, with the backward pass dominating runtime in the post-densification refinement phase. We observe substantial update redundancy in this phase: many sampled views have near-plateaued losses and provide diminishing gradient benefits, but standard training still runs full backpropagation. We propose SkipGS with a novel view-adaptive backward gating mechanism for efficient post-densification training. SkipGS always performs the forward pass to update per-view loss statistics, and selectively skips backward passes when the sampled view's loss is consistent with its recent per-view baseline, while enforcing a minimum backward budget for stable optimization. On Mip-NeRF 360, compared to 3DGS, SkipGS reduces end-to-end training time by 23.1%, driven by a 42.0% reduction in post-densification time, with comparable reconstruction quality. Because it only changes when to backpropagate -- without modifying the renderer, representation, or loss -- SkipGS is plug-and-play and compatible with other complementary efficiency strategies for additive speedups.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2603.08997 [cs.CV] |
| (or arXiv:2603.08997v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2603.08997
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View a PDF of the paper titled SkipGS: Post-Densification Backward Skipping for Efficient 3DGS Training, by Jingxing Li and 2 other authors
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